Unraveling the causal association between leukocyte telomere length and infertility: A two-sample Mendelian randomization study.

Infertility is a significant challenge in modern society, and observed studies have reported the association between telomere length and infertility. Whether this relationship is causal remains controversial.We employed two-sample mendelian randomization (MR) to investigate the causal relationship between leukocyte telomere length (LTL) and major causes of infertility, including male and female infertility, sperm abnormalities, and endometriosis. MR analyses were mainly performed using the inverse variance weighted (IVW) method and complemented with other MR methods.Our findings demonstrate a causal association between LTL and endometriosis (OR1.304, 95% CI (1.122,1.517), p = 0.001), suggesting its potential as a biomarker for this condition. However, we did not observe a significant causal relationship between LTL and other infertility causes.Our study presents compelling evidence on the relationship between LTL and endometriosis. Meanwhile, our study demonstrates that there is no causal relationship between LTL and infertility. This research contributes to the field by shedding light on the importance of LTL in the early diagnosis and intervention of endometriosis.

LIVE: a manually curated encyclopedia of experimentally validated interactions of lncRNAs.

Advances in studies of long noncoding RNAs (lncRNAs) have provided data regarding the regulatory roles of lncRNAs, which perform functional roles through interactions with other functional elements. To track the underlying relationships among lncRNAs, various databases have been developed as repositories for lncRNA data. However, the ability to comprehensively explore the diverse interactions between lncRNAs and other functional elements is limited. To this end, we developed LIVE (LncRNA Interaction Validated Encyclopaedia), an interactive resource to integrate the diverse interactions of functional elements with lncRNAs. LIVE is a manually curated database of experimentally validated interactions of lncRNAs with genes, proteins and other various functional elements. By mining publications, we constructed LIVE with the following three interaction networks: a binding interaction network, a regulation network and a disease network; then, we combined them to form a comprehensive lncRNA interaction network. The current release of LIVE contains the validated interactions of 572 lncRNAs in humans and mice with 103 proteins, 209 genes, 56 transcription factors and 194 diseases. LIVE provides an interactive interface with charts and figures to aid users in searching and browsing interactions with lncRNAs. LIVE will greatly facilitate further investigation into the regulatory roles of lncRNAs and is freely available.

The landscape of the A-to-I RNA editome from 462 human genomes.

A-to-I editing, as a post-transcriptional modification process mediated by ADAR, plays a crucial role in many biological processes in metazoans. However, how and to what extent A-to-I editing diversifies and shapes population diversity at the RNA level are largely unknown. Here, we used 462 mRNA-sequencing samples from five populations of the Geuvadis Project and identified 16,518 A-to-I editing sites, with false detection rate of 1.03%. These sites form the landscape of the RNA editome of the human genome. By exploring RNA editing within and between populations, we revealed the geographic restriction of rare editing sites and population-specific patterns of edQTL editing sites. Moreover, we showed that RNA editing can be used to characterize the subtle but substantial diversity between different populations, especially those from different continents. Taken together, our results demonstrated that the nature and structure of populations at the RNA level are illustrated well by RNA editing, which provides insights into the process of how A-to-I editing shapes population diversity at the transcriptomic level. Our work will facilitate the understanding of the landscape of the RNA editome at the population scale and will be helpful for interpreting differences in the distribution and prevalence of disease among individuals and across populations.

Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

Lnc2Catlas: an atlas of long noncoding RNAs associated with risk of cancers.

Lnc2Catlas ( http://lnc2catlas.bioinfotech.org/ ) is an atlas of long noncoding RNAs (lncRNAs) associated with cancer risk. LncRNAs are a class of functional noncoding RNAs with lengths over 200 nt and play a vital role in diverse biological processes. Increasing evidence shows that lncRNA dysfunction is associated with many human cancers/diseases. It is therefore important to understand the underlying relationship between lncRNAs and cancers. To this end, we developed Lnc2Catlas to compile quantitative associations between lncRNAs and cancers using three computational methods, assessing secondary structure disruption, lncRNA-protein interactions, and co-expression networks. Lnc2Catlas was constructed based on 27,670 well-annotated lncRNAs, 31,749,216 SNPs, 1,473 cancer-associated proteins, and 10,539 expression profiles of 33 cancers from The Cancer Genome Atlas (TCGA). Lnc2Catlas contains 247,124 lncRNA-SNP pairs, over two millions lncRNA-protein interactions, and 6,902 co-expression clusters. We deposited Lnc2Catlas on Alibaba Cloud and developed interactive, mobile device-compatible, user-friendly interfaces to help users search and browse Lnc2Catlas with ultra-low latency. Lnc2Catlas can aid in the investigation of associations between lncRNAs and cancers and can provide candidate lncRNAs for further experimental validation. Lnc2Catlas will facilitate an understanding of the associations between lncRNAs and cancer and will help reveal the critical role of lncRNAs in cancer.

Functional annotation of structural ncRNAs within enhancer RNAs in the human genome: implications for human disease.

Enhancer RNAs (eRNAs) are a novel class of non-coding RNA (ncRNA) molecules transcribed from the DNA sequences of enhancer regions. Despite extensive efforts devoted to revealing the potential functions and underlying mechanisms of eRNAs, it remains an open question whether eRNAs are mere transcriptional noise or relevant biologically functional species. Here, we identified a catalogue of eRNAs in a broad range of human cell/tissue types and extended our understanding of eRNAs by demonstrating their multi-omic signatures. Gene Ontology (GO) analysis revealed that eRNAs play key roles in human cell identity. Furthermore, we detected numerous known and novel functional RNA structures within eRNA regions. To better characterize the cis-regulatory effects of non-coding variation in these structural ncRNAs, we performed a comprehensive analysis of the genetic variants of structural ncRNAs in eRNA regions that are associated with inflammatory autoimmune diseases. Disease-associated variants of the structural ncRNAs were disproportionately enriched in immune-specific cell types. We also identified riboSNitches in lymphoid eRNAs and investigated the potential pathogenic mechanisms by which eRNAs might function in autoimmune diseases. Collectively, our findings offer valuable insights into the function of eRNAs and suggest that eRNAs might be effective diagnostic and therapeutic targets for human diseases.